Overview
On Site
$$Negotiable
Accepts corp to corp applications
Contract - Independent
Contract - W2
Contract - 6 Month(s)
Skills
Machine Learning
Artificial Intelligence
NLP
LLMs
Job Details
AI ML Lead
Dallas, Texas
Contract
We are looking for a talented and motivated AI/ML Engineer to join our team and help design, develop, and deploy cutting-edge machine learning models and AI solutions. The ideal candidate has strong experience in data science, machine learning engineering, and a proven track record of delivering production-grade ML models.
Key Responsibilities:
- Design, build, and deploy scalable machine learning models and pipelines.
- Collaborate with data scientists, software engineers, and product teams to deliver AI-powered solutions.
- Preprocess, clean, and analyze large datasets for training and inference.
- Develop APIs and integrate ML models into production systems.
- Monitor and retrain models as needed to ensure performance over time.
- Document experiments, processes, and model performance for reproducibility.
Required Skills:
- Strong programming skills in Python (NumPy, Pandas, Scikit-learn).
- Hands-on experience with ML/DL frameworks: TensorFlow, PyTorch, or Keras.
- Experience in training, validating, and deploying ML models.
- Understanding of ML concepts: classification, regression, clustering, recommendation systems.
- Experience with cloud platforms: AWS (SageMaker), Google Cloud Platform, or Azure.
- Knowledge of Docker, Git, and CI/CD pipelines.
Preferred Skills (Nice to Have):
- Experience with NLP, Computer Vision, or Generative AI.
- Familiarity with MLOps tools: MLflow, Kubeflow, Airflow.
- Experience with large language models (LLMs) and transformers (Hugging Face).
- Exposure to data engineering tools (Spark, Kafka, Snowflake).
Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.